Linear Processes for Functional Data
Year: 2009
Type: preprint
Abstract: Linear processes on functional spaces were born about fifteen years ago. And this original topic went through the same fast development as the other areas of functional data modeling such as PCA or regression. They aim at generalizing to random curves the classical ARMA models widely known in time series analysis. They offer a wide spectrum of models suited to the statistical inference on continuous time stochastic processes within the paradigm of functional data. Essentially designed to improve the quality and the range of prediction, they give birth to challenging theoretical and applied problems. We propose here a state of the art which emphasizes recent advances and we present some promising perspectives based on our experience in this area.
Source: arXiv (Cornell University)
Authors: Mas, André, Pumo, Besnik
Institution: Institut National d'Hygiène du Maroc
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6929310531
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